AI Finds Hidden Patterns, Trends and Problems in Huge Datasets

By Stephanie Ramirez

Finding regularities, patterns and trends in massive amounts of seemingly unrelated data is a strength of AI and machine learning.

Artificial intelligence (AI) used with machine learning and other automated applications has found its way into almost every industry, including direct selling. Data analysis tools built with AI technology can provide forecasting, predict future customer and distributor behavior, provide restocking and supply chain levels, target campaigns with extreme personalization, create enhanced distributor and customer journeys, and even monitor the brand. 

The applications are endless because the technology is based on algorithmic formulas—a set of “rules” or instructions to be followed that are coded into problem-solving software. Algorithms range from simple, such as assigning a new online customer to a consultant in the same zip code, to complex, such as predicting a frustrated consultant is about to quit the business based on the recognition of specific past behaviors. 

Finding regularities, patterns and trends in massive amounts of seemingly unrelated data is a strength of AI and machine learning. Without this technology, the collection and analysis of data points and the ability to find commonalities—ranging from emails opened and videos watched to ordering patterns, recruiting, product preferences, and many, many more—would be impossible.

The larger the data set provided to the AI-powered software, the better the outcome, says Michel Bayan, CEO of software firm DirecTech Labs. Bayan’s firm has collected over a billion data points in the past six years, all of which can be crunched by the software to create categories of people who have hundreds of data points in common.

Supply Chain Efficiency

Since machine learning works by using algorithms, it naturally omits any emotional bias to its predictions. This is especially beneficial when it comes to creating stable forecasting for product manufacturing, says Emily Mahana, director of supply chain management for Nu Skin. 

Mahana says the company’s AI tools highlighted their tendency to lean toward “emotional forecasting” on particular products. The company is now able to use its historical data trends to predict future demand and balance any forecasting biases. She says, “We are seeing a more accurate picture of sales demands, unbuffered by fear, and we have taken action to adjust supply plans for actual demand.” 

Continuous learning and becoming “smarter” is a hallmark of AI, and one of its strongest benefits. Working on a “loop” of ingesting and analyzing past data and new data, with new data being added in real-time on a daily basis, the software becomes “better” at making predictions. 

Mahana says the team at Nu Skin has held “man versus machine” competitions over the last several months that “clearly show the machine’s continuous improvement in predicting demand with the input of more quality data.”

In the end, she says the benefit of using machine learning in forecasting allows Nu Skin to build supply chain plans that “ keep our product availability high and our inventory value lean.”

Helping Distributors

Onboarding new distributors well is critical to their future success, and AI-powered tools can aid that process too. Matt Lind, CEO of Krato, a provider of customizable applications for direct sellers, says, “It can be overwhelming to a new distributor to learn everything they need to know about the business, compensation plan and products in their first 90 days. Utilizing AI allows a company to push out automated messages, tailored directly to where those distributors are in the business onboarding process. When you can connect with them on a personal level, they are much more likely to succeed.”

Not only does AI-powered software become better at making predictions through its continuous evaluation of historical and new data, it also provides distributors with valuable information they need to run their individual businesses more efficiently. Lind says that putting relevant data and business intelligence in the hands of distributors in a way they can digest it accomplishes a two-fold purpose: customer acquisition and retention as well as distributor acquisition.

Lind says, “Using machine learning, all the data that is collected from customer behaviors allows you to create messages or tasks specifically tailored and automatically sent to a distributor. For example, a prompt might encourage the distributor to reach out to specific customers who haven’t ordered in a while with a precrafted message.”

AI’s ability to pull out trends and patterns that are deeply buried in the massive amounts of data available to a company allows for a tailored approach that would be impossible to conceive of without it. 

Bayan says, “Instead of grouping people based on one or two different data points, we consider hundreds of data points, and then the software groups people together who have relatively the same types of behavior.”

This analysis has allowed his company to create segmented categories of consumers far beyond the most common “customer” or “distributor” labels. Their data indicated that most direct selling companies have about nine different segments of behaviors that can be identified. As a result, companies are able to identify critical moments in a customer’s or a distributor’s journey and send them highly personalized and targeted messages at specific times. The result can be a decrease in churn, and an increase in sales and retention. 

“Companies can then create training, email campaigns, onboarding programs or special promotions specific for each one of those groups and communicate with one-to-one personalized messaging,” says Bayan.

The depth to which AI can assist in mining and analyzing data helps keep the corporate office informed with critical information. Mahana says, “We are discovering an intimate map of our customers’ journey through the data mining and automation process.”

This map, she says, also helps to predict short- and long-term customer needs. The team is working to identify what leading indicators they can use to “create an automated, real-time reporting structure to alert supply teams and vendors of changing consumer behavior.” 

Interacting Through Chatbots

As technology has evolved, distributors and customers can now take advantage of yet another product of AI—chatbots. A “chatbot” is a machine that has conversations with people, either by texting or messaging through a computer, or by voice over the phone. AI-powered chatbots can mimic human intelligence with speech recognition abilities, but go even farther by “learning” from previous conversations the bot may have had with that specific customer, or by accessing information from all customers in general.

Perhaps the most common examples of sophisticated chatbots (or virtual assistants) are the over 121 million smart speaker devices in U.S. homes. Whether it’s named Siri, Cortana, Alexa or Watson, each is programmed to “learn” and become increasingly smarter about their owners’ preferences over time. It’s not surprising that businesses also choose to capitalize on that capability.

According to Vince Han, founder and CEO of chatbot platform MobileCoach, utilizing chatbots allow companies to have the same type of conversation all over the world, at any given time, and in many different languages without having to employ thousands of people to support all those customer and distributor inquiries.

“A chatbot can be present 24 hours a day answering questions, as well as encouraging and holding that consultant accountable. It can provide automatic coaching through an ongoing conversation,” says Han. 

He adds, “This generation of consumers expects immediate resolution, immediate answers to their questions, and any consumer-focused company that can deliver on those things is going to excel.”

Brand Monitoring

With the FTC keeping its eye on the direct selling channel at the moment, compliance is top of mind for many direct sellers, and AI is helping direct sellers police their field. AI is being used to search the web for anything that is public-facing and collects data points specifically related to certain incidents they see occuring that may be a violation of policies.

Company compliance officers are then armed with the information necessary to compel a distributor to remove or modify a piece of content that the company has deemed a violation.

“Machine learning can help direct selling companies make sure their representatives and field leaders aren’t making any claims about the products or business opportunity that would be an FTC violation,” says Zach Arrington, program director for online monitoring software firm Momentum Factor, whose software uses machine learning on its massive database of direct sales compliance violations collected over the years to better understand what constitutes a compliance incident. It then leverages that knowledge to analyze millions of social media posts online for risky content and report that back to the compliance department. 

“Every direct selling company that’s been around for a while has mountains of data,” says Arrington. “And being able to use some machine learning to distill insights about customers is priceless. If companies aren’t taking advantage of that, they’re missing out.”

Indeed, according to Sebastian Leonardi, president at DSXgroup, 57 percent of executives in the entire retail sector—which includes direct sellers—believe that AI will bring the biggest benefit to improving the customer experience and enhancing personalization. 

He says, “Companies that have deployed architecture and technology that gives them better access to data are going to win big with AI.” 

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